Generative Artificial Intelligence for Personalized Elderly Care through the lens of the Hedonic Motivation System Adoption Model

This research aimed to create and evaluate a hedonic motivation model to comprehend elderly citizens’ acceptance of generative artificial intelligence. The age-related cognitive ability traits and perceived boredom of the elderly were added to the earlier technology acceptance model in the proposed hedonic motivation and flow model. The theoretical model was empirically evaluated using survey questionnaire data of 314 respondents over 55 years. The result showed that cognitive ability significantly impacted perceived ease-of-use, impacting all five hedonic variables. Flow experience was impacted by curiosity and joy but not by control. It was also found that perceived usefulness did not impact usage, while boredom and flow experience impacted usage of GAI-assisted personalized elderly care. The study stated implications for theory and practice before concluding with future research directions.